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We propose Februus; a new idea to neutralize highly potent and insidious Trojan attacks on Deep Neural Network (DNN) systems at run-time.
L. van der Maaten and G. Hinton, “Visualizing data using t-SNE,” Journal of Machine Learning Research , 2008
2008
Earlier work this paper cites.
A. Krizhevsky, G. Hinton et al. , “Learning multiple layers of features from tiny images,” 2009
2009
Earlier work this paper cites.
Y. LeCun, C. Cortes, and C. Burges, “Mnist handwritten digit database,” ATT Labs , 2010
2010
Earlier work this paper cites.
J. Stallkamp, M. Schlipsing, J. Salmen, and C. Igel, “Man vs. computer: Benchmarking machine learning algorithms for traffic sign recognition,” Neural Networks , 2012
2012
Earlier work this paper cites.
M. Mathias, R. Timofte, R. Benenson, and L. Van Gool, “Traffic sign recognition — how far are we from the solution?” in International Joint Conference on Neural Networks (IJCNN) , 2013
2013
Earlier work this paper cites.
Y. Taigman, M. Yang, M. Ranzato, and L. Wolf, “Deepface: Closing the gap to human-level performance in face verification,” in Conference on Computer Vision and Pattern Recognition (CVPR) , 2014
2014
Earlier work this paper cites.
Z. Yuan, Y. Lu, Z. Wang, and Y. Xue, “Droid-sec: deep learning in android malware detection,” in ACM conference on SIGCOMM , 2014
2014
Earlier work this paper cites.
C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus, “Intriguing properties of neural networks,” in International Conference on Learning Representations (ICLR) , 2014
2014
Earlier work this paper cites.
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial nets,” in Advances in Neural Information Processing Systems (NeurIPS) , 2014
2014
Earlier work this paper cites.
C. Chen, A. Seff, A. Kornhauser, and J. Xiao, “Deepdriving: Learning affordance for direct perception in autonomous driving,” in IEEE International Conference on Computer Vision (ICCV) , 2015
2015
Earlier work this paper cites.
K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” in International Conference on Learning Representations (ICLR) , 2015
2015
Earlier work this paper cites.
O. M. Parkhi, A. Vedaldi, and A. Zisserman, “Deep face recognition,” in British Machine Vision Conference (BMVC) , 2015
2015
Earlier work this paper cites.
I. Goodfellow, J. Shlens, and C. Szegedy, “Explaining and harnessing adversarial examples,” in International Conference on Learning Representations (ICLR) , 2015
2015
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” 2016
2016
Earlier work this paper cites.
Q. Wang, W. Guo, K. Zhang, A. G. Ororbia, II, X. Xing, X. Liu, and C. L. Giles, “Adversary resistant deep neural networks with an application to malware detection,” in ACM SIGKDD International Conference on Knowledge Discovery and Data Mining , 2017
2017
Earlier work this paper cites.
X. Chen, C. Liu, B. Li, K. Lu, and D. Song, “Targeted backdoor attacks on deep learning systems using data poisoning,” 2017
2017
Earlier work this paper cites.
W. Samek, A. Binder, G. Montavon, S. Lapuschkin, and K. Müller, “Evaluating the visualization of what a deep neural network has learned,” IEEE Transactions on Neural Networks and Learning Systems , 2017
2017
Cited alongside, same era.
R. R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, and D. Batra, “Grad-cam: Visual explanations from deep networks via gradient-based localization,” in IEEE International Conference on Computer Vision (ICCV) , 2017
2017
Cited alongside, same era.
S. Iizuka, E. Simo-Serra, and H. Ishikawa, “Globally and locally consistent image completion,” ACM Transactions on Graphics , 2017
2017
Cited alongside, same era.
I. Gulrajani, F. Ahmed, M. Arjovsky, V. Dumoulin, and A. C. Courville, “Improved training of wasserstein gans,” in Advances in Neural Information Processing Systems (NeurIPS) , 2017
2017
Cited alongside, same era.
H. Chen, C. Fu, J. Zhao, and F. Koushanfar, “Deepinspect: A black-box trojan detection and mitigation framework for deep neural networks,” in International Joint Conference on Artificial Intelligence IJCAI , 2019
2019
Closest in time.
W. Guo, L. Wang, X. Xing, M. Du, and D. Song, “Tabor: A highly accurate approach to inspecting and restoring trojan backdoors in ai systems,” 2019
2019
Closest in time.
Y. Liu, W.-C. Lee, G. Tao, S. Ma, Y. Aafer, and X. Zhang, “ABS: Scanning neural networks for back-doors by artificial brain stimulation,” in ACM conference on Computer and Communications Security (CCS) , 2019
2019
Closest in time.
B. Chen, W. Carvalho, N. Baracaldo, H. Ludwig, B. Edwards, T. Lee, I. Molloy, and B. Srivastava, “Detecting backdoor attacks on deep neural networks by activation clustering,” in Artificial Intelligence Safety Workshop at Association for the Advancement of Artificial Intelligence (AAAI) , 2019
2019
Closest in time.
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Y. Liu, Y. Xie, and A. Srivastava, “Neural trojans,” in 2017 IEEE International Conference on Computer Design (ICCD) , 2017
2017
Cited alongside, same era.
S. M. Anwar, M. Majid, A. Qayyum, M. Awais, M. Alnowami, and M. K. Khan, “Medical image analysis using convolutional neural networks: a review,” Journal of medical systems , 2018
2018
Cited alongside, same era.
Y. Liu, S. Ma, Y. Aafer, W.-C. Lee, J. Zhai, W. Wang, and X. Zhang, “Trojaning attack on neural networks,” in Network and Distributed System Security Symposium (NDSS) , 2018
2018
Cited alongside, same era.
C. Wierzynski, “The challenges and opportunities of explainable ai,” 2018. [Online]. Available: https://www.intel.ai/the-challenges-and-opportunities-of-explainable-ai
2018
Cited alongside, same era.
K. Liu, B. Dolan-Gavitt, and S. Garg, “Fine-pruning: Defending against backdooring attacks on deep neural networks,” in International Symposium on Research in Attacks, Intrusions, and Defenses (RAID) , 2018
2018
Cited alongside, same era.
Q. Cao, L. Shen, W. Xie, O. M. Parkhi, and A. Zisserman, “Vggface2: A dataset for recognising faces across pose and age,” in IEEE International Conference on Automatic Face and Gesture Recognition (FG) , 2018
2018
Cited alongside, same era.
A. Madry, A. Makelov, L. Schmidt, D. Tsipras, and A. Vladu, “Towards deep learning models resistant to adversarial attacks,” in International Conference on Learning Representations (ICLR) , 2018
2018
Cited alongside, same era.
S.-C. Lin, Y. Zhang, C.-H. Hsu, M. Skach, M. E. Haque, L. Tang, and J. Mars, “The architectural implications of autonomous driving: Constraints and acceleration,” in ACM Special Interest Group on Programming Languages (SIGPLAN) Notices , 2018
2018
Cited alongside, same era.
Y. Gao, Y. Kim, B. G. Doan, Z. Zhang, G. Zhang, S. Nepal, D. C. Ranasinghe, and H. Kim, “Design and evaluation of a multi-domain trojan detection method on deep neural networks,” 2019
2019
Closest in time.
E. Bagdasaryan, A. Veit, Y. Hua, D. Estrin, and V. Shmatikov, “How to backdoor federated learning,” in International Conference on Artificial Intelligence and Statistics (AISTATS) , 2020
2020
Closest in time.
E. Chou, F. Tramèr, and G. Pellegrino, “Sentinet: Detecting physical attacks against deep learning systems,” in Deep Learning and Security Workshop at IEEE Security and Privacy (S&P) , 2020
2020
Closest in time.
X. Zhang, N. Wang, H. Shen, S. Ji, X. Luo, and T. Wang, “Interpretable deep learning under fire,” in USENIX Security Symposium , 2020
2020
Closest in time.
S. Li, M. Xue, B. Zhao, H. Zhu, and X. Zhang, “Invisible backdoor attacks on deep neural networks via steganography and regularization,” IEEE Transactions on Dependable and Secure Computing (TDSC) , 2020
2020
Closest in time.
Y. Liu, X. Ma, J. Bailey, and F. Lu, “Reflection backdoor: A natural backdoor attack on deep neural networks,” in European Conference on Computer Vision (ECCV) , 2020
2020
Closest in time.
A. Saha, A. Subramanya, and H. Pirsiavash, “Hidden trigger backdoor attacks,” in Association for the Advancement of Artificial Intelligence (AAAI) , 2020
2020
Closest in time.
E. Bagdasaryan and V. Shmatikov, “Blind backdoors in deep learning models,” 2020
2020
Closest in time.
Z. Zhang, J. Jia, B. Wang, and N. Z. Gong, “Backdoor attacks to graph neural networks,” 2020
2020
Closest in time.
M. Weber, X. Xu, B. Karlas, C. Zhang, and B. Li, “Rab: Provable robustness against backdoor attacks,” 2020
2020
Closest in time.
B. Wang, X. Cao, J. jia, and N. Z. Gong, “On certifying robustness against backdoor attacks via randomized smoothing,” 2020
2020
Closest in time.